Despite challenging time constraints on the project, the team successfully delivered results within the given timeframe. They closely collaborated with the client while creating the new software. They made new approaches and learned new skills to complete the project.

• They first converted the JSON files into CSV format. They had to create separate CSV files for nodes and relationship data. Then, they implemented a Neo4j admin tool to import the new CSV files. It allowed them to insert around 70 million nodes and 200 million pieces of relationship data in five to six minutes instead of hours.
• Since the queries for generating the graphs were mostly OLAP queries, they denormalized the data and precalculated expensive aggregations. They then stored the data and aggregations on new nodes and relationships.
• The team implemented a full-text search on the nodes using Lucene indices and a distance-similarity calculation, and they could fetch the results in little time.
• To streamline the knowledge graph generation, the team used only one component each for tables, line charts, and bar charts. The team used a general approach by ignoring the designs attached to each graph type and dynamically passing those to the desired components.